Feature Selection for Tabular Foundation Models: Exploring Feature Utility in Low-Data Molecular Prediction

arXiv:2609.30789 · cs.LG · Submitted 2026-09-25 · Read on arXiv

cs.LG

Submitted: 2026-09-25

Updated: 2026-10-09

Code: https://github.com/ersilia-os/chemicl

Terminology

• AUC — 46× in this paper · explained in 2 episodes
• Provenance — 8× in this paper · explained in 4 episodes
• Accuracy — 7× in this paper · explained in 1 episode
• Foundation Model — 6× in this paper · explained in 3 episodes
• Benchmark — 5× in this paper · explained in 2 episodes
• Embeddings — 3× in this paper · explained in 2 episodes
• Foundation Models — 3× in this paper · explained in 7 episodes
• Core — 1× in this paper · explained in 2 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Degeneracy — 1× in this paper · explained in 3 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Regret — 1× in this paper · explained in 3 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes

Sources

• Deep Learning Foundation Models from Classical Molecular Descriptors
• Can Tabular In-Context Learners Generalize to Biomolecular Property Prediction?
• Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: The Predictive Cost of ELBO Binding
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks